Nw Buddy Wave Knowledge logo

Nw Buddy Wave Knowledge

Organization
nWave-ai
nw-buddy-wave-knowledge

Wave methodology knowledge for the buddy agent — what each wave does, its inputs and outputs, and how to route questions.

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-buddy-wave-knowledge
Stars
616
Forks
65
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Buddy Wave Knowledge AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-buddy-wave-knowledge
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-buddy-wave-knowledge .claude/skills/nw-buddy-wave-knowledge
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Buddy Wave Knowledge in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nw Buddy Wave Knowledge on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nw Buddy Wave Knowledge is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Wave Methodology Knowledge

The nWave methodology organizes work into a canonical sequence of waves. Each wave has a purpose, a primary agent, inputs from earlier waves, and outputs consumed by later waves. The buddy agent uses this map to answer "where am I in the process" and "what should I do next" questions without stepping into execution territory.

The canonical wave sequence

DISCOVER -> DISCUSS -> SPIKE(opt) -> DESIGN -> DEVOPS -> DISTILL -> DELIVER

Each wave has a slash command (/nw-<wave>) and a primary agent. Waves run top-to-bottom. Skipping waves is a smell; going back to revise an earlier wave is normal and expected. SPIKE is optional — include it when validating a new mechanism, performance requirement, or external integration.

Wave-by-wave reference

1. DISCOVER

  • Purpose: validate that an opportunity exists and is worth pursuing.
  • Primary agent: product-discoverer.
  • Inputs: a rough idea, a user complaint, a market signal, or a strategic prompt.
  • Outputs: an evidence brief — problem statement, target users, pains, existing solutions, strength of signal, go/no-go recommendation.
  • Typical artifacts: docs/discover/<opportunity>-brief.md, user interview notes, competitive scans.
  • Common questions: "is this worth doing?", "who has this problem?", "what's the evidence?"

2. DISCUSS

  • Purpose: turn a validated opportunity into user stories with acceptance criteria.
  • Primary agent: product-owner.
  • Inputs: DISCOVER output — validated problem and target users.
  • Outputs: a set of user stories, each with a goal, acceptance criteria in Given-When-Then form, and a rough priority.
  • Typical artifacts: docs/discuss/<feature>-stories.md, a backlog update.
  • Common questions: "what does 'done' look like for this feature?", "what are the user stories?"

3. SPIKE (optional)

  • Purpose: validate one core assumption through timeboxed throwaway code before investing in architecture design.
  • Primary agent: software-crafter.
  • Inputs: DISCUSS output — stories, acceptance criteria, and assumptions to test.
  • Outputs: spike findings documenting what works, what assumptions were wrong, performance measurements. Code is discarded.
  • Typical artifacts: docs/feature/<name>/spike/findings.md, throwaway code (not committed).
  • Common questions: "will this mechanism work?", "can we hit the performance budget?", "does the third-party API behave as expected?"
  • When to run: Include SPIKE when the feature involves a new mechanism never tried before, a performance requirement that can't be validated by reasoning alone, or an external integration with unknown behavior. Skip for pure refactoring, bug fixes, or features < 1 day.
  • Duration: max 1 hour, timeboxed.

4. DESIGN

  • Purpose: propose the solution architecture — component boundaries, key abstractions, major trade-offs.
  • Primary agent: solution-architect.
  • Inputs: DISCUSS output — stories and acceptance criteria. SPIKE findings (if spike was run) — validated assumptions and performance constraints.
  • Outputs: an architecture proposal, usually updating the SSOT architecture doc, plus ADRs for significant decisions.
  • Typical artifacts: docs/architecture/architecture-design.md updates, docs/adrs/ADR-NNN-<title>.md, diagrams.
  • Common questions: "how will this be built?", "what are the components?", "what are the boundaries?"

5. DEVOPS

  • Purpose: plan the infrastructure, CI/CD, and deployment needed to run what DESIGN proposed.
  • Primary agent: platform-architect.
  • Inputs: DESIGN output.
  • Outputs: infrastructure plan, CI/CD changes, deployment checklist, rollback plan.
  • Typical artifacts: updated CI workflow files, IaC changes, runbooks.
  • Common questions: "how do we ship this?", "what does CI need?", "what's the rollback plan?"

6. DISTILL

  • Purpose: translate stories and acceptance criteria into executable BDD test scenarios — the specification the crafter will implement against.
  • Primary agent: acceptance-designer.
  • Inputs: DISCUSS stories and DESIGN architecture.
  • Outputs: tests/acceptance/ files with Given-When-Then scenarios, tagged with @skip initially, plus a roadmap of delivery steps.
  • Typical artifacts: feature files or test classes with BDD scenarios, a delivery roadmap in docs/feature/<name>/roadmap.md.
  • Common questions: "what are the test scenarios?", "what's the delivery plan?"

7. DELIVER

  • Purpose: implement the feature using Outside-In TDD, step by step, until all DISTILL scenarios pass.
  • Primary agent: software-crafter.
  • Inputs: DISTILL output — scenarios and roadmap.
  • Outputs: working, tested, committed code.
  • Typical artifacts: commits following the TDD 3-phase canon (RED -> GREEN -> COMMIT, ADR-025 2026-05-07; legacy 5-phase PREPARE -> RED_ACCEPTANCE -> RED_UNIT -> GREEN -> COMMIT preserved for pre-2026-05-07 audit-log replay), updated tests, updated source files.
  • Common questions: "is this feature done?", "what step are we on?", "is the test suite green?"

Cross-wave agents

Some agents operate across waves:

  • researcher — gathers evidence for any wave that needs it.
  • troubleshooter — diagnoses problems in existing code or processes.
  • documentarist — produces user-facing documentation, typically after DELIVER.
  • visual-architect — produces diagrams to support DESIGN.

Peer reviewers exist for each specialist (one per wave) and enforce quality gates.

Routing questions to the right wave

When a user asks something, the buddy identifies which wave owns the question and answers from that wave's artifacts. Examples:

QuestionWaveWhere to read
"Is this idea any good?"DISCOVERdiscover briefs
"What are the user stories?"DISCUSSstory docs / backlog
"How will the module be shaped?"DESIGNarchitecture doc, ADRs
"What's the CI plan?"DEVOPSCI workflows, runbooks
"What are the test scenarios?"DISTILLfeature files, roadmap
"What step are we on?"DELIVERcommits, test suite, roadmap

If the user's question spans multiple waves (e.g., "what's this feature and how does it work?"), answer with contributions from each relevant wave, in order.

Recognizing which wave the user is in

Signals:

  • DISCOVER: user is asking about opportunity, not code. Words like "should we", "is there demand".
  • DISCUSS: user is talking about stories, acceptance criteria, user needs.
  • SPIKE: user is asking about validating assumptions, prototyping a mechanism, or performance testing. Words like "prove it works", "test this idea", "spike".
  • DESIGN: user is asking about components, layers, boundaries, trade-offs.
  • DEVOPS: user is talking about deployment, CI, environments, secrets, rollout.
  • DISTILL: user is asking about test scenarios, Given-When-Then, the roadmap.
  • DELIVER: user is asking about implementation status, failing tests, next step, commits.

If unsure, ask.

What the buddy does NOT do

  • Does not run the crafter. The buddy is read-only guidance. If the user wants code written, they should invoke /nw-deliver or the crafter agent directly.
  • Does not skip waves. If a user asks to "just implement this" and DISTILL hasn't been run, the buddy points out the gap and suggests running DISTILL first.
  • Does not invent artifacts. If a DESIGN doc doesn't exist, the buddy says so — it doesn't make one up.
  • Does not write acceptance tests on the fly. That's DISTILL's job.
  • Does not change the wave order. The sequence exists because each wave depends on the previous.

Rule of thumb

The buddy's mental model is always: "What wave is this question in, which files hold the answer, and what are the gaps?" Answer from those files, cite them, and flag gaps as findings.

Frequently asked questions

What does the Nw Buddy Wave Knowledge AI skill do?

Wave methodology knowledge for the buddy agent — what each wave does, its inputs and outputs, and how to route questions.

Why use Nw Buddy Wave Knowledge on TypingMind?

Because you install it once and use it with any model. Nw Buddy Wave Knowledge is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nw Buddy Wave Knowledge in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-buddy-wave-knowledge. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Buddy Wave Knowledge?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nw Buddy Wave Knowledge?

As many as you like. As long as a model supports skills, you can use Nw Buddy Wave Knowledge with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nw Buddy Wave Knowledge AI skill free?

Yes. It is published on GitHub by nWave-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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